Estimating the daily trend in the size of the COVID-19 infected population in Wuhan

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Abstract

Background

The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10 940 confirmed cases outside Hubei province.

Methods

In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan.

Results

We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [ CI]: 3139–3321) and 51 273 (95% CI: 49 844–52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5–1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0–40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4–73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8–100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019.

Conclusions

In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

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Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Qun Li, Xuhua Guan, Peng Wu … (2020)

Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)

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Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

Joseph Wu, Kathy Leung, Gabriel Leung (2020)

Summary Background Since Dec 31, 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia caused by the 2019 novel coronavirus (2019-nCoV). Cases have been exported to other Chinese cities, as well as internationally, threatening to trigger a global outbreak. Here, we provide an estimate of the size of the epidemic in Wuhan on the basis of the number of cases exported from Wuhan to cities outside mainland China and forecast the extent of the domestic and global public health risks of epidemics, accounting for social and non-pharmaceutical prevention interventions. Methods We used data from Dec 31, 2019, to Jan 28, 2020, on the number of cases exported from Wuhan internationally (known days of symptom onset from Dec 25, 2019, to Jan 19, 2020) to infer the number of infections in Wuhan from Dec 1, 2019, to Jan 25, 2020. Cases exported domestically were then estimated. We forecasted the national and global spread of 2019-nCoV, accounting for the effect of the metropolitan-wide quarantine of Wuhan and surrounding cities, which began Jan 23–24, 2020. We used data on monthly flight bookings from the Official Aviation Guide and data on human mobility across more than 300 prefecture-level cities in mainland China from the Tencent database. Data on confirmed cases were obtained from the reports published by the Chinese Center for Disease Control and Prevention. Serial interval estimates were based on previous studies of severe acute respiratory syndrome coronavirus (SARS-CoV). A susceptible-exposed-infectious-recovered metapopulation model was used to simulate the epidemics across all major cities in China. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credibile interval (CrI). Findings In our baseline scenario, we estimated that the basic reproductive number for 2019-nCoV was 2·68 (95% CrI 2·47–2·86) and that 75 815 individuals (95% CrI 37 304–130 330) have been infected in Wuhan as of Jan 25, 2020. The epidemic doubling time was 6·4 days (95% CrI 5·8–7·1). We estimated that in the baseline scenario, Chongqing, Beijing, Shanghai, Guangzhou, and Shenzhen had imported 461 (95% CrI 227–805), 113 (57–193), 98 (49–168), 111 (56–191), and 80 (40–139) infections from Wuhan, respectively. If the transmissibility of 2019-nCoV were similar everywhere domestically and over time, we inferred that epidemics are already growing exponentially in multiple major cities of China with a lag time behind the Wuhan outbreak of about 1–2 weeks. Interpretation Given that 2019-nCoV is no longer contained within Wuhan, other major Chinese cities are probably sustaining localised outbreaks. Large cities overseas with close transport links to China could also become outbreak epicentres, unless substantial public health interventions at both the population and personal levels are implemented immediately. Independent self-sustaining outbreaks in major cities globally could become inevitable because of substantial exportation of presymptomatic cases and in the absence of large-scale public health interventions. Preparedness plans and mitigation interventions should be readied for quick deployment globally. Funding Health and Medical Research Fund (Hong Kong, China).

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The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights

Hiroshi Nishiura, Tetsuro Kobayashi, Yichi Yang … (2020)

From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the virus. Assuming that the mean detection window of the virus can be informed by the mean serial interval (estimated at 7.5 days), the ascertainment rate of infection was estimated at 9.2% (95% confidence interval: 5.0, 20.0). This indicates that the incidence of infection in Wuhan can be estimated at 20,767 infected individuals, including those with asymptomatic and mildly symptomatic infections. The infection fatality risk (IFR)—the actual risk of death among all infected individuals—is therefore 0.3% to 0.6%, which may be comparable to Asian influenza pandemic of 1957–1958.

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Author and article information

Contributors

Qiu-Shi Lin: ethanlin@pku.edu.cn

Tao-Jun Hu: 1500013356@pku.edu.cn

Xiao-Hua Zhou:

ORCID: http://orcid.org/0000-0001-7935-1222

azhou@math.pku.edu.cn

Journal

Journal ID (nlm-ta): Infect Dis Poverty

Journal ID (iso-abbrev): Infect Dis Poverty

Title: Infectious Diseases of Poverty

Publisher: BioMed Central (London )

ISSN (Print): 2095-5162

ISSN (Electronic): 2049-9957

Publication date (Electronic): 18 June 2020

Publication date PMC-release: 18 June 2020

Publication date Collection: 2020

Volume: 9

Electronic Location Identifier: 69

Affiliations

[1 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Beijing International Center for Mathematical Research, , Peking University, ; Beijing, 100871 China

[2 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, School of Mathematical Sciences, , Peking University, ; Beijing, 100871 China

[3 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Department of Biostatistics, School of Public Health, , Peking University, ; Beijing, 100191 China

[4 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Center for Statistical Science, , Peking University, ; Beijing, 100871 China

Author information

Xiao-Hua Zhou http://orcid.org/0000-0001-7935-1222

Article

Publisher ID: 693

DOI: 10.1186/s40249-020-00693-4

PMC ID: 7301350

PubMed ID: 32552794

SO-VID: 311a27f9-7cec-4344-916d-410611c1ade7

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 7 March 2020

Date accepted : 10 June 2020

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Keywords: covid-19,wuhan,daily,trend,size,infection

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Keywords: covid-19, wuhan, daily, trend, size, infection

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Estimating the daily trend in the size of the COVID-19 infected population in Wuhan

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Abstract

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Novel Coronavirus Disease COVID-19

Most cited references 5

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study

The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights

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